Process evaluation of appreciative inquiry to translate pain management evidence into pediatric nursing practice

Background Appreciative inquiry (AI) is an innovative knowledge translation (KT) intervention that is compatible with the Promoting Action on Research in Health Services (PARiHS) framework. This study explored the innovative use of AI as a theoretically based KT intervention applied to a clinical issue in an inpatient pediatric care setting. The implementation of AI was explored in terms of its acceptability, fidelity, and feasibility as a KT intervention in pain management. Methods A mixed-methods case study design was used. The case was a surgical unit in a pediatric academic-affiliated hospital. The sample consisted of nurses in leadership positions and staff nurses interested in the study. Data on the AI intervention implementation were collected by digitally recording the AI sessions, maintaining logs, and conducting individual semistructured interviews. Data were analysed using qualitative and quantitative content analyses and descriptive statistics. Findings were triangulated in the discussion. Results Three nurse leaders and nine staff members participated in the study. Participants were generally satisfied with the intervention, which consisted of four 3-hour, interactive AI sessions delivered over two weeks to promote change based on positive examples of pain management in the unit and staff implementation of an action plan. The AI sessions were delivered with high fidelity and 11 of 12 participants attended all four sessions, where they developed an action plan to enhance evidence-based pain assessment documentation. Participants labeled AI a 'refreshing approach to change' because it was positive, democratic, and built on existing practices. Several barriers affected their implementation of the action plan, including a context of change overload, logistics, busyness, and a lack of organised follow-up. Conclusions Results of this case study supported the acceptability, fidelity, and feasibility of AI as a KT intervention in pain management. The AI intervention requires minor refinements (e.g., incorporating continued follow-up meetings) to enhance its clinical utility and sustainability. The implementation process and effectiveness of the modified AI intervention require evaluation in a larger multisite study

A novel long non-coding natural antisense RNA is a negative regulator of Nos1 gene expression

Long non-coding natural antisense transcripts (NATs) are widespread in eukaryotic species. Although recent studies indicate that long NATs are engaged in the regulation of gene expression, the precise functional roles of the vast majority of them are unknown. Here we report that a long NAT (Mm-antiNos1 RNA) complementary to mRNA encoding the neuronal isoform of nitric oxide synthase (Nos1) is expressed in the mouse brain and is transcribed from the non-template strand of the Nos1 locus. Nos1 produces nitric oxide (NO), a major signaling molecule in the CNS implicated in many important functions including neuronal differentiation and memory formation. We show that the newly discovered NAT negatively regulates Nos1 gene expression. Moreover, our quantitative studies of the temporal expression profiles of Mm-antiNos1 RNA in the mouse brain during embryonic development and postnatal life indicate that it may be involved in the regulation of NO-dependent neurogenesis

Three-dimensional localization of ultracold atoms in an optical disordered potential

We report a study of three-dimensional (3D) localization of ultracold atoms suspended against gravity, and released in a 3D optical disordered potential with short correlation lengths in all directions. We observe density profiles composed of a steady localized part and a diffusive part. Our observations are compatible with the self-consistent theory of Anderson localization, taking into account the specific features of the experiment, and in particular the broad energy distribution of the atoms placed in the disordered potential. The localization we observe cannot be interpreted as trapping of particles with energy below the classical percolation threshold.Comment: published in Nature Physics; The present version is the initial manuscript (unchanged compared to version 1); The published version is available online at http://www.nature.com/nphys/journal/vaop/ncurrent/full/nphys2256.htm

The role of Comprehension in Requirements and Implications for Use Case Descriptions

Within requirements engineering it is generally accepted that in writing specifications (or indeed any requirements phase document), one attempts to produce an artefact which will be simple to comprehend for the user. That is, whether the document is intended for customers to validate requirements, or engineers to understand what the design must deliver, comprehension is an important goal for the author. Indeed, advice on producing ‘readable’ or ‘understandable’ documents is often included in courses on requirements engineering. However, few researchers, particularly within the software engineering domain, have attempted either to define or to understand the nature of comprehension and it’s implications for guidance on the production of quality requirements. Therefore, this paper examines thoroughly the nature of textual comprehension, drawing heavily from research in discourse process, and suggests some implications for requirements (and other) software documentation. In essence, we find that the guidance on writing requirements, often prevalent within software engineering, may be based upon assumptions which are an oversimplification of the nature of comprehension. Hence, the paper examines guidelines which have been proposed, in this case for use case descriptions, and the extent to which they agree with discourse process theory; before suggesting refinements to the guidelines which attempt to utilise lessons learned from our richer understanding of the underlying discourse process theory. For example, we suggest subtly different sets of writing guidelines for the different tasks of requirements, specification and design

Pirfenidone in idiopathic pulmonary fibrosis:expert panel discussion on the management of drug-related adverse events

Pirfenidone is currently the only approved therapy for idiopathic pulmonary fibrosis, following studies demonstrating that treatment reduces the decline in lung function and improves progression-free survival. Although generally well tolerated, a minority of patients discontinue therapy due to gastrointestinal and skin-related adverse events (AEs). This review summarizes recommendations based on existing guidelines, research evidence, and consensus opinions of expert authors, with the aim of providing practicing physicians with the specific clinical information needed to educate the patient and better manage pirfenidone-related AEs with continued pirfenidone treatment. The main recommendations to help prevent and/or mitigate gastrointestinal and skin-related AEs include taking pirfenidone during (or after) a meal, avoiding sun exposure, wearing protective clothing, and applying a broad-spectrum sunscreen with high ultraviolet (UV) A and UVB protection. These measures can help optimize AE management, which is key to maintaining patients on an optimal treatment dose.Correction in: Advances in Therapy, Volume 31, Issue 5, pp 575-576 , doi: 10.1007/s12325-014-0118-8</p

Absence of neuronal autoantibodies in neuropsychiatric systemic lupus erythematosus

International audienceThis study aimed to characterise both neuronal autoantibodies and levels of interferon α, two proposed causative agents in neuropsychiatric systemic lupus erythematosus (NPSLE). Cerebrospinal fluid (CSF) and plasma from 35 patients with systemic lupus erythematosus (SLE; 15 with NPSLE) showed no antibodies against natively expressed N-methyl-D-aspartate receptors (NMDARs), or the surface of live hippocampal neurons. By comparison to controls (n = 104), patients with SLE had antibodies that bound to a peptide representing the extracellular domain of NMDARs (p < 0.0001), however, binding was retained against both rearranged peptides and no peptide (r = 0.85 and r = 0.79, respectively, p < 0.0001). In summary, neuronal-surface reactive antibodies were not detected in NPSLE. Further, while interferon α levels were higher in SLE (p < 0.0001), they lacked specificity for NPSLE. Our findings mandate a search for novel biomarkers in this condition. ANN NEUROL 2020

In silico assessment of potential druggable pockets on the surface of α1-Antitrypsin conformers

The search for druggable pockets on the surface of a protein is often performed on a single conformer, treated as a rigid body. Transient druggable pockets may be missed in this approach. Here, we describe a methodology for systematic in silico analysis of surface clefts across multiple conformers of the metastable protein α1-antitrypsin (A1AT). Pathological mutations disturb the conformational landscape of A1AT, triggering polymerisation that leads to emphysema and hepatic cirrhosis. Computational screens for small molecule inhibitors of polymerisation have generally focused on one major druggable site visible in all crystal structures of native A1AT. In an alternative approach, we scan all surface clefts observed in crystal structures of A1AT and in 100 computationally produced conformers, mimicking the native solution ensemble. We assess the persistence, variability and druggability of these pockets. Finally, we employ molecular docking using publicly available libraries of small molecules to explore scaffold preferences for each site. Our approach identifies a number of novel target sites for drug design. In particular one transient site shows favourable characteristics for druggability due to high enclosure and hydrophobicity. Hits against this and other druggable sites achieve docking scores corresponding to a Kd in the µM–nM range, comparing favourably with a recently identified promising lead. Preliminary ThermoFluor studies support the docking predictions. In conclusion, our strategy shows considerable promise compared with the conventional single pocket/single conformer approach to in silico screening. Our best-scoring ligands warrant further experimental investigation

A comparative analysis of predictive models of morbidity in intensive care unit after cardiac surgery – Part II: an illustrative example

<p>Abstract</p> <p>Background</p> <p>Popular predictive models for estimating morbidity probability after heart surgery are compared critically in a unitary framework. The study is divided into two parts. In the first part modelling techniques and intrinsic strengths and weaknesses of different approaches were discussed from a theoretical point of view. In this second part the performances of the same models are evaluated in an illustrative example.</p> <p>Methods</p> <p>Eight models were developed: Bayes linear and quadratic models, <it>k</it>-nearest neighbour model, logistic regression model, Higgins and direct scoring systems and two feed-forward artificial neural networks with one and two layers. Cardiovascular, respiratory, neurological, renal, infectious and hemorrhagic complications were defined as morbidity. Training and testing sets each of 545 cases were used. The optimal set of predictors was chosen among a collection of 78 preoperative, intraoperative and postoperative variables by a stepwise procedure. Discrimination and calibration were evaluated by the area under the receiver operating characteristic curve and Hosmer-Lemeshow goodness-of-fit test, respectively.</p> <p>Results</p> <p>Scoring systems and the logistic regression model required the largest set of predictors, while Bayesian and <it>k</it>-nearest neighbour models were much more parsimonious. In testing data, all models showed acceptable discrimination capacities, however the Bayes quadratic model, using only three predictors, provided the best performance. All models showed satisfactory generalization ability: again the Bayes quadratic model exhibited the best generalization, while artificial neural networks and scoring systems gave the worst results. Finally, poor calibration was obtained when using scoring systems, <it>k</it>-nearest neighbour model and artificial neural networks, while Bayes (after recalibration) and logistic regression models gave adequate results.</p> <p>Conclusion</p> <p>Although all the predictive models showed acceptable discrimination performance in the example considered, the Bayes and logistic regression models seemed better than the others, because they also had good generalization and calibration. The Bayes quadratic model seemed to be a convincing alternative to the much more usual Bayes linear and logistic regression models. It showed its capacity to identify a minimum core of predictors generally recognized as essential to pragmatically evaluate the risk of developing morbidity after heart surgery.</p

The predictive role of serum and bronchoalveolar lavage cytokines and adhesion molecules for acute respiratory distress syndrome development and outcome

BACKGROUND: The predictive role of many cytokines and adhesion molecules has not been studied systematically in acute respiratory distress syndrome (ARDS). METHODS: We measured prospectively tumour necrosis factor alpha (TNF-α), interleukin (IL)-1, soluble vascular adhesion molecule-1 (VCAM-1) and soluble intercellular adhesion molecule-1 (ICAM-1) in serum and bronchoalveolar lavage fluid (BALF) within 2 hours following admission, in 65 patients. The patients were divided into: those fulfilling the criteria for ARDS (n = 23, group A), those who were pre-ARDS and who developed ARDS within 24 hours (n = 14, group B), and those on pre-ARDS but who never developed ARDS (n = 28, group C). RESULTS: All the measured molecules were only found at higher levels in the serum of patients that died either with or without ARDS (P < 0.05 – P < 0.0001). Patients at risk exhibited a good negative predictive value (NPV) of the measured molecules for ARDS development both in their serum (89 to 95%) and BALF (86 to 92%) levels. In contrast to BALF, serum levels of IL-1 and adhesion molecules exhibited a good NPV (68 to 96%), sensitivity (60 to 88%) and survival specificity (74 to 96%) in all groups. All molecules in serum and BALF IL-1 were correlated with the APACHE II (P < 0.05 – P < 0.0001). Serum and BALF IL-1 as well as BALF TNF-α were negatively correlated to PaO(2)/FiO(2) (all P < 0.05). CONCLUSIONS: The studied molecules have good NPV for ARDS development both in serum and BALF. Serum rather than BALF levels seem to be related to outcome